s ome observations on the design of early stage clinical trials in the pharmaceutical industry

1. some observations on the design of early stage clinical trials in the pharmaceutical industry Hans Hockey Biometrics Matters Limited (BML) 13 Nevada Road Hamilton 3216 New Zealand hans@biometricsmatters.com IBC, Kobe, Japan, August 2012 EMR-IBS, Tel Aviv, 23 April 2013

2. Structure of talk

3. Structure of talk • There is no structure!

4. Five Case Studies • Two doses plus placebo • “Factorial” dose escalation and food effect • 3-treatment, 3-period cross-overdesign • Escalating dose study with placebo substitution plus • Augmented placebo insertion and food effect

5. Take-home points Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

6. Take-home point 1 Use contrasts that are orthogonal, and are models, not tests. Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

7. Case Study 1

8. Case Study 1

9. Case Study 1

10. Case Study 1

11. Case Study 1 A modeller, not a tester, would prefer even more dose levels

14. Take-home point 2 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

15. A typical Phase 1 drug development program list of studies Over 50 studies, many are escalating doses or 2x2 crossover designs

18. Ronald Fisher argued in 1926 that "complex" designs (such as factorial designs) were more efficient than studying one factor at a time. Fisher wrote, "No aphorism is more frequently repeated in connection with field trials, than that we must ask Nature few questions, or, ideally, one question, at a time. The writer is convinced that this view is wholly mistaken." Nature, he suggests, will best respond to "a logical and carefully thought out questionnaire". A factorial design allows the effect of several factors and even interactions between them to be determined with the same number of trials as are necessary to determine any one of the effects by itself with the same degree of accuracy. (Wikipedia) Combining studies

20. Combining studies (part 1) Case Study 2

21. A Phase 1 PK study Design 1 • Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses Fed v fasted always a 2x2 crossover Too long! (7-day washout three times)

22. A Phase 1 PK study Design 2 • Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses

23. A Phase 1 PK study Design 2 • Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses

24. A Phase 1 PK study Design 3 • Three doses by two food regimes. Use all 6 combinations? Both food regimes at all 3 doses

25. A Phase 1 PK study – Design 3 4, not 6, treatments (6 not 12 cells) Only one washout period Safe escalation What assumptions? Two analyses? NO!

26. Should one 3x3 cross-over study with N subjects replace two 2x2 cross-over studies with 2N subjects total? • That is, why not compare A & B & C together instead of A & B separately from A & C? • Are three periods too long? • Worry that both the A-B and A-C comparisons depend on A treatment being well estimated • A typical example: A - market formulation (fasted) • B - research formulation (fasted) • C - market formulation (fed) Combining studies (part 2)

28. Case Study 3 Capsule v tablet & Fed v fast for capsules

29. A B Food effect Bioequivalence C

31. A1 A2 Food effect (A v B) Formulation effect (1 v 2) B1 B2

32. NOT a Case Study Not seen by me anyhow

33. Take-home point 3 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

34. Take-home point 3 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Not every PK crossover subject needs a placebo Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

35. Case Study 4 Design: This is a single-blind, placebo-controlled, randomised, “cross-over”, single-dose escalation study in which the toleration, safety and pharmacokinetics of XYZ-123,456 will be investigated. Both ‘cross-over’ and escalation? Two groups or cohorts, 8 each

36. Group A doses (mg) over time 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg)

37. Placebo insertion Placebo insertion design 8 subjects per dose, 8 per Placebo (Same pattern for Group B doses, but 2.5, 10 & 40 mg)

38. Placebo substitution 4 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons)

39. Why not Placebo substitution plus! 6 subjects per dose, all 6 per Placebo (Placebo is of least interest for PK, but needed for safety comparisons) 6 per treatment!

40. “¾ placebo substitution” Embrace imbalance!

41. ¾ placebo substitution Embrace imbalance! (but it isn’t unbalanced! – it’s a BIBD)

42. Take-home point 4 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

43. Case Study 5 Still don’t worry about imbalance (it also is a BIBD, but plus extra replications of non-placebo sequences) Do worry about practical issues (in this case, not sure if 10, 11 or 12 pre-screened subjects will turn up on Day 1, and less on Day 2)

44. Augmented insertion design for 12 subjects over 4 sessions in 2 days Ex = Food (fEd) with x capsules Ax = Fasted (fAst) with x capsules (from final protocol)

45. Practical Design Issues • The design includes placebo insertion such that placebo occurs twice in each of 4 sessions, with double blinding. Each subject has exposure to each active dose, with subjects 9-12 receiving the maximum of two 8-capsule sessions in the one day, after having been exposed to 4 capsules the previous test day. Total exposure ranges from 2 to 16 capsules per day per subject. • The design is robust to not having all planned 12 subjects available as there is double replication of the sequences 9 and 10 (sequences 11 and 12). Random allocation of sequences to subjects will be arranged such that if there is a shortfall of 1 or 2 subjects then sequence 12 and then 11 will not be allocated. If there is a further shortfall (very unlikely) then all missing Day 1 subjects will be replaced. • All sequences/subjects include the highest dose on Day 2, so given that 10 to 12 subjects completed Day 1, there is no imperative to replace subjects if up to 2 do not attend Day 2. If the Day 2 discontinuation rate is higher though, then consideration will again be given to subject replacement.

46. Take-home point 5 Use contrasts that are orthogonal, and are models, not tests Design programs to include more combined studies, including factorial and similar designs Don’t worry about ‘imbalance’ Practical matters can matter more than statistical issues In cross-over designs, never ever fit Sequence Use mixed, not fixed, models for maximum information extraction

47. Advantages of cross-overs Using within subject variation gives increased precision and power Lower costs (usually extra subjects more expensive than extra periods)

48. Advantages of cross-overs Using within subject variation gives increased precision and power Lower costs Disadvantage of 2x2 cross-overs Sequence, carryover and treatment.period are all aliased

49. 2x2 cross-over ANOVA table (12 subjects)

50. 2x2 cross-over ANOVA table (12 subjects)